Legal claims defining the scope of protection. Each claim is shown in both the original legal language and a plain English translation.
1. A method of bid testing, the method comprising: executing, on a processor of a computing device, computer instructions of a bid test component that cause the computing device to perform operations, the operations comprising: executing an application programming interface (API) to poll a storage repository of a content recommendation auction engine over a network and detecting that a content scheme, used by the content recommendation auction engine to facilitate bidding on opportunities recommending content of the content scheme to users of a content provider service, has been tagged with an indicator triggering the content recommendation auction engine transmitting a notification to the bid test component; receiving, over the network by the bid test component, the notification comprising a description of content, a user audience, and a budget of the content scheme; in response to determining that the indicator corresponds to a command keyword recognized by the bid test component, determining a number of test content schemes to create and test budget values to assign to each test content scheme based upon the description of content, the user audience, and the budget; generating the number of test content schemes and assigning bid level values and portions of the budget as the test budget values to each test content scheme; calculating estimated budget utilization values for each of the test content schemes, wherein a first estimated budget utilization value corresponds to a first percentage of a first budget, assigned to a first test content scheme, that is to be utilized by a first point in time, and wherein a second estimated budget utilization value corresponds to a second percentage of a second budget, assigned to a second test content scheme, that is to be utilized by a second point in time; transmitting, over the network by the bid test component, the test content schemes to the content recommendation auction engine that implements bidding on opportunities to recommend content to users of the content provider service based upon the test content schemes; triggering, based upon the indicator, the content recommendation auction engine to accept instructions from the bid test component for implementing the test content schemes, wherein the computing device executes the bid testing component transmitting the instructions over the network to the content recommendation auction engine and remotely controlling how the content recommendation auction engine implements the test content schemes bidding on the opportunities to recommend the content to the users; determining test content scheme statistic data, received over the network by the bid test component from the content recommendation auction engine, derived from bidding data of the test content schemes being implemented and user action data for the content; analyzing the test content scheme statistic data for ranking the bid level values assigned to the test content schemes, wherein the first test content scheme is assigned a first rank based upon a first efficiency of the first test content scheme utilizing the first budget in relation to the first estimated budget utilization value, and wherein the second test content scheme is assigned a second rank based upon a second efficiency of the second test content scheme utilizing the second budget in relation to the second estimated budget utilization value; dynamically adjusting test budget values assigned to the test content schemes based upon the rankings; deactivating test content schemes having performance below a threshold based upon the rankings, wherein the first test content scheme is deactivated based upon the first rank being below a threshold and the second test content scheme is maintained based upon the second ranking not being below the threshold; and providing a recommendation, specifying optimal performance bidding parameters, bidding timeframes, and bid level values to implement for the content scheme, based upon ranked bid level values.
This invention relates to a system for optimizing content bidding in a content recommendation auction engine. The system addresses the challenge of efficiently testing and refining bidding strategies to maximize the performance of content recommendations for users of a content provider service. The method involves a bid test component that interacts with a content recommendation auction engine to evaluate different bidding approaches. The bid test component monitors a storage repository of the auction engine to detect when a content scheme is tagged with a specific indicator, triggering a notification that includes details such as content description, target audience, and budget. Upon recognizing a command keyword in the indicator, the component determines the number of test content schemes to create and assigns test budget values based on the provided parameters. It generates multiple test schemes with varying bid levels and budget allocations, then calculates estimated budget utilization for each scheme at different time points. The test schemes are transmitted to the auction engine, which implements them for bidding on content recommendation opportunities. The bid test component remotely controls the auction engine's execution of these schemes, collecting bidding and user action data to analyze performance. The system ranks the test schemes based on efficiency, dynamically adjusting budgets and deactivating underperforming schemes. Finally, it provides recommendations for optimal bidding parameters, timeframes, and bid levels to improve the original content scheme's performance. This automated approach ensures data-driven optimization of content bidding strategies.
2. The method of claim 1 , wherein the notification comprises a recommended bid level range specified by the content recommendation auction engine for the content scheme, and the operations comprising: determining the bid level values based upon the recommended bid level range.
This invention relates to content recommendation systems, specifically improving bid management in auction-based content delivery. The problem addressed is the inefficiency in determining optimal bid levels for content recommendations, which can lead to either overspending or underperforming ad placements. The system includes a content recommendation auction engine that generates a notification containing a recommended bid level range for a content scheme. The method involves receiving this notification and determining specific bid level values based on the provided range. The bid level values are then used to adjust bidding strategies dynamically, ensuring competitive yet cost-effective placement of content. The content recommendation auction engine evaluates various factors, such as user engagement metrics, content relevance, and market competition, to derive the recommended bid level range. The method further includes analyzing historical bid performance data to refine the bid level values within the recommended range, optimizing the balance between cost and visibility. By leveraging the recommended bid level range, the system automates bid adjustments, reducing manual intervention and improving auction performance. This approach enhances the efficiency of content delivery while maintaining competitive positioning in real-time auctions. The solution is particularly useful in digital advertising, where precise bid management directly impacts campaign success.
3. The method of claim 1 , wherein the notification comprises a recommended bid level value specified by the content recommendation auction engine for the content scheme, and the operations comprising: determining the bid level values based upon the recommended bid level value.
This invention relates to content recommendation systems, specifically improving bid-level determination in auction-based content recommendation engines. The problem addressed is the inefficiency in dynamically adjusting bid levels for content recommendations, which can lead to suboptimal placement and revenue. The system includes a content recommendation auction engine that processes content schemes (e.g., ad placements) and generates a recommended bid level value for each scheme. The method involves determining bid level values for the content scheme based on this recommended value. The engine evaluates factors such as content relevance, user engagement metrics, and historical performance to generate the recommendation. The bid level values are then adjusted in real-time to optimize placement and maximize revenue. The method ensures that bid levels are dynamically aligned with the engine's recommendations, improving the efficiency of content delivery. This approach enhances the accuracy of bid adjustments, leading to better content placement and higher engagement. The system may also incorporate feedback loops to refine future recommendations based on performance data. The invention is particularly useful in digital advertising, where precise bid management is critical for maximizing return on investment.
4. The method of claim 1 , wherein the operations comprise: evaluating historic content scheme performance data to determine the number of test content schemes to create.
A system and method for optimizing content delivery involves dynamically generating and testing multiple content schemes to improve performance. The technology addresses the challenge of selecting the most effective content presentation strategies in digital platforms, such as websites or applications, to enhance user engagement and conversion rates. The method evaluates historical performance data of past content schemes to determine the optimal number of new test content schemes to generate. These test schemes are then deployed to different user segments, and their performance is monitored in real-time. The system collects data on user interactions, such as click-through rates, dwell time, and conversion rates, to assess the effectiveness of each scheme. Based on this analysis, the system identifies the highest-performing content schemes and automatically adjusts future content delivery to prioritize these schemes. The method also includes adaptive learning mechanisms that refine the selection criteria over time, ensuring continuous improvement in content performance. This approach eliminates the need for manual A/B testing and provides a scalable solution for optimizing digital content delivery.
5. The method of claim 1 , wherein the operations comprise: evaluating historic content scheme performance data to determine the test budget values.
This invention relates to optimizing content delivery systems by dynamically adjusting test budgets for different content schemes based on historical performance data. The core problem addressed is inefficient allocation of testing resources, where content schemes may be over-tested or under-tested due to static budgeting approaches, leading to suboptimal user engagement and system performance. The method involves analyzing historical performance data of various content schemes to assess their past effectiveness. This data includes metrics such as user engagement, conversion rates, and other relevant performance indicators. By evaluating this historical data, the system determines optimal test budget values for each content scheme. These budget values dictate the proportion of resources (e.g., computational power, bandwidth, or testing cycles) allocated to each scheme during future testing phases. The goal is to prioritize schemes that have shown higher performance historically while reducing resources for underperforming schemes, thereby improving overall system efficiency and effectiveness. The method may also involve integrating real-time feedback mechanisms to continuously refine budget allocations as new performance data becomes available. This adaptive approach ensures that the system remains responsive to changing user preferences and content trends. The invention is particularly useful in digital advertising, recommendation systems, and other content-driven platforms where dynamic optimization of testing resources is critical.
6. The method of claim 1 , wherein the operations comprise: assigning a first test budget value to the first test content scheme; and assigning a second test budget value, different than the first test budget value, to the second test content scheme.
This invention relates to a method for optimizing test content distribution in a testing system, particularly for allocating test budgets across different test content schemes. The problem addressed is the inefficient allocation of testing resources, which can lead to suboptimal testing outcomes or wasted resources. The method involves dynamically assigning distinct test budget values to different test content schemes to improve testing efficiency and effectiveness. The method operates by first assigning a first test budget value to a first test content scheme and a second, different test budget value to a second test content scheme. The test content schemes may include different types of test content, such as question sets, assessment formats, or testing methodologies. By allocating different budget values, the system can prioritize certain test content schemes over others based on factors like importance, difficulty, or historical performance. This dynamic allocation ensures that testing resources are used more effectively, leading to better test outcomes and reduced waste. The method may also involve adjusting the budget values in real-time based on feedback or performance metrics, allowing for continuous optimization of the testing process. This approach is particularly useful in educational or certification testing environments where different test content schemes require varying levels of attention and resources. The invention improves upon prior systems by providing a more flexible and adaptive way to manage test budgets, enhancing overall testing efficiency.
7. The method of claim 1 , wherein the operations comprise: determining a budget reserve amount; and assigning test budget values totaling a difference between the budget and the budget reserve amount.
This invention relates to budget allocation in testing systems, particularly for optimizing resource distribution while maintaining a reserve for unexpected needs. The method addresses the challenge of efficiently allocating limited testing resources while ensuring flexibility to handle unforeseen requirements. The process begins by determining a budget reserve amount, which is a portion of the total budget set aside for contingencies. The remaining budget is then distributed as test budget values across different testing operations. These values are assigned such that their sum equals the difference between the total budget and the reserved amount. This ensures that while most of the budget is allocated to planned testing activities, a reserve is maintained for adjustments or additional testing needs that may arise during the process. The method may also involve dynamically adjusting the budget reserve amount based on factors such as testing progress, resource availability, or changes in project requirements. This adaptability helps maintain efficiency while accommodating real-world testing scenarios. The approach is particularly useful in environments where testing resources are constrained, and flexibility is required to respond to evolving conditions.
8. The method of claim 1 , wherein the operations comprise: providing a second recommendation to allocate a first portion of the budget for use in an automatic bidding option provided by the content recommendation auction engine and a second portion of the budget and a bid level value for use in a manual bidding option provided by the content recommendation auction engine.
This invention relates to budget allocation strategies in content recommendation auction systems, addressing the challenge of optimizing ad spend across automated and manual bidding options. The method involves distributing a budget between two bidding approaches: an automatic bidding option, where the system dynamically adjusts bids based on predefined rules or algorithms, and a manual bidding option, where advertisers set specific bid levels. The system allocates a first portion of the budget to the automatic bidding option, allowing the auction engine to manage bids autonomously. A second portion of the budget, along with a predefined bid level value, is allocated to the manual bidding option, enabling advertisers to control bids for specific content or placements. This dual allocation approach balances automation with advertiser control, improving campaign efficiency and performance. The method ensures that the budget is split strategically, leveraging the strengths of both bidding modes to maximize return on investment. The system may also adjust allocations dynamically based on performance metrics or predefined criteria, further optimizing ad spend. This solution is particularly useful in digital advertising platforms where advertisers need flexibility in budget management while benefiting from automated optimization.
9. A computing device comprising: a processor; and memory comprising processor-executable instructions of a bid testing component that when executed by the processor cause performance of operations, the operations comprising: executing an application programming interface (API) to poll a storage repository of a content recommendation auction engine hosted by a remote computing device, remote to the computing device executing the processor-executable instructions of the bid testing component, over a network and detecting that a content scheme, used by the content recommendation auction engine to facilitate bidding on opportunities recommending content of the content scheme to users of a content provider service, has been tagged with an indicator triggering the content recommendation auction engine transmitting a notification to the bid test component; receiving, over the network by the bid test component, the notification comprising a description of content, a user audience, and a budget of the content scheme; in response to determining that the indicator corresponds to a command keyword recognized by the bid test component, determining a number of test content schemes to create and test budget values to assign to each test content scheme based upon the description of content, the user audience, and the budget; generating the number of test content schemes and assigning bid level values and portions of the budget as the test budget values to each test content scheme; calculating estimated budget utilization values for each of the test content schemes, wherein a first estimated budget utilization value corresponds to a first percentage of a first budget, assigned to a first test content scheme, that is to be utilized by a first point in time, and wherein a second estimated budget utilization value corresponds to a second percentage of a second budget, assigned to a second test content scheme, that is to be utilized by a second point in time; transmitting, over the network by the bid test component, the test content schemes to the content recommendation auction engine that implements bidding on opportunities to recommend content to users of the content provider service based upon the test content schemes; triggering, based upon the indicator, the content recommendation auction engine to accept instructions from the bid test component for implementing the test content schemes, wherein the computing device executes the bid testing component transmitting the instructions over the network to the content recommendation auction engine and remotely controlling how the content recommendation auction engine implements the test content schemes bidding on the opportunities to recommend the content to the users; determining test content scheme statistic data, received over the network by the bid test component from the content recommendation auction engine, derived from bidding data of the test content schemes being implemented and user action data for the content; analyzing the test content scheme statistic data for ranking the bid level values assigned to the test content schemes, wherein the first test content scheme is assigned a first rank based upon a first efficiency of the first test content scheme utilizing the first budget in relation to the first estimated budget utilization value, and wherein the second test content scheme is assigned a second rank based upon a second efficiency of the second test content scheme utilizing the second budget in relation to the second estimated budget utilization value; dynamically adjusting test budget values assigned to the test content schemes based upon the rankings; deactivating test content schemes having performance below a threshold based upon the rankings, wherein the first test content scheme is deactivated based upon the first rank being below a threshold and the second test content scheme is maintained based upon the second ranking not being below the threshold; and providing a recommendation, specifying optimal performance bidding parameters, bidding timeframes, and bid level values to implement for the content scheme, based upon ranked bid level values.
This invention relates to a system for optimizing content recommendation bidding in an auction-based advertising environment. The system addresses the challenge of efficiently testing and refining bidding strategies to maximize the performance of content recommendations for users of a content provider service. A computing device includes a processor and memory storing a bid testing component that interacts with a remote content recommendation auction engine. The bid testing component polls the auction engine to detect when a content scheme is tagged for testing. Upon detection, the component receives a notification containing content details, target audience, and budget. Recognizing a command keyword, the system determines the number of test content schemes to create and assigns budget values to each. It generates test schemes with different bid levels and calculates estimated budget utilization for each. The test schemes are transmitted to the auction engine, which implements them based on instructions from the bid testing component. The system collects performance data, ranks the test schemes based on efficiency, and dynamically adjusts budgets accordingly. Underperforming schemes are deactivated, while high-performing ones are maintained. Finally, the system provides recommendations for optimal bidding parameters, timeframes, and bid levels to improve the original content scheme's performance. This approach automates the testing and optimization of bidding strategies, enhancing the effectiveness of content recommendations.
10. The computing device of claim 9 , wherein the operations comprise: implementing an automated bidding scheme to generate and transmit content schemes from the bid test component over the network to the content recommendation auction engine to implement in place of the content scheme; and adjusting the content schemes, dynamically during implementation by the content recommendation auction engine, based upon monitored content scheme statistic data for the content schemes.
This invention relates to computing devices that manage content recommendation systems, particularly those using auction-based mechanisms. The problem addressed is the static nature of content recommendation schemes, which often fail to adapt to real-time performance data, leading to suboptimal content delivery and user engagement. The computing device includes a bid test component that generates and transmits alternative content schemes to a content recommendation auction engine over a network. These schemes are implemented in place of the existing content scheme. The system dynamically adjusts the content schemes during their execution based on monitored performance statistics, such as user engagement metrics or conversion rates. This allows for real-time optimization of content recommendations, improving relevance and effectiveness. The bid test component may also generate multiple content schemes for simultaneous testing, enabling A/B or multivariate testing to determine the most effective approach. The adjustments are made without manual intervention, ensuring continuous improvement based on real-time data. This automated bidding scheme enhances the adaptability of content recommendation systems, making them more responsive to user behavior and market conditions. The system is particularly useful in digital advertising, personalized content delivery, and recommendation engines where dynamic optimization is critical.
11. The computing device of claim 9 , wherein the operations comprise: providing a second recommendation of a first bid level value for a first time period and a second bid level value, different than the first bid level value, for a second time period.
This invention relates to computing devices configured to optimize bidding strategies for online advertising or auctions. The problem addressed is the need to dynamically adjust bid levels over different time periods to improve campaign performance, such as maximizing impressions or conversions while minimizing costs. The computing device includes a processor and memory storing instructions that, when executed, perform operations to generate and provide bid recommendations. These operations include analyzing historical bid data, user behavior, or market conditions to determine optimal bid levels for different time periods. The device provides a second recommendation that specifies a first bid level value for a first time period and a second, different bid level value for a second time period. This allows advertisers to adjust bids dynamically, such as increasing bids during high-traffic periods and reducing them during low-traffic periods, to optimize cost efficiency and campaign outcomes. The system may also incorporate machine learning models to predict the most effective bid levels based on real-time data. The invention improves upon static bidding strategies by enabling time-based adjustments tailored to fluctuating demand or user activity patterns.
12. The computing device of claim 9 , wherein the operations comprise: providing a second recommendation of a first budget allocation for a first time period and a second budget allocation, different than the first budget allocation, for a second time period.
This invention relates to computing devices that generate budget recommendations for users. The problem addressed is the need for dynamic budget allocation over time to optimize financial planning. The computing device analyzes financial data to provide tailored budget recommendations that adjust based on changing circumstances. Specifically, the device generates a second recommendation that includes a first budget allocation for a first time period and a second, different budget allocation for a second time period. This allows users to adapt their spending or savings strategies as conditions evolve. The system may also incorporate user preferences, historical spending patterns, and external factors like economic trends to refine recommendations. The goal is to help users make informed financial decisions by offering flexible, time-sensitive budgeting guidance. The computing device may further include features like automated tracking of expenses, alerts for budget deviations, and integration with financial accounts to ensure accurate and up-to-date recommendations. This approach improves traditional static budgeting methods by introducing adaptability and personalized insights.
13. The computing device of claim 9 , wherein the operations comprise: providing a second recommendation of bid level values and budget allocations based upon demographics of the user audience.
This invention relates to computing devices configured to optimize advertising bid levels and budget allocations for digital advertising campaigns. The system addresses the challenge of efficiently targeting user audiences by dynamically adjusting bid strategies based on demographic data. The computing device processes user audience demographics to generate tailored recommendations for bid level values and budget allocations, ensuring that advertising resources are allocated effectively to reach the most relevant audience segments. The device may also analyze historical performance data to refine these recommendations, improving campaign efficiency and return on investment. By integrating demographic insights with bid optimization, the system enhances the precision of ad targeting and maximizes the impact of advertising budgets. The invention builds on a computing device that receives user audience data and generates initial bid recommendations, further refining these recommendations by incorporating demographic factors such as age, gender, location, or other relevant attributes. This approach ensures that advertising campaigns are optimized for specific audience segments, leading to higher engagement and conversion rates. The system may also include mechanisms to adjust recommendations in real-time based on changing audience demographics or campaign performance metrics.
14. A non-transitory machine readable medium having stored thereon processor-executable instructions of a bid test component that when executed cause performance of operations, the operations comprising: executing an application programming interface (API) to poll a storage repository of a content recommendation auction engine over a network and detecting that a content scheme, used by the content recommendation auction engine to facilitate bidding on opportunities recommending content of the content scheme to users of a content provider service, has been tagged with an indicator triggering the content recommendation auction engine transmitting a notification to the bid test component; receiving, over the network by the bid test component, the notification comprising a description of content, a user audience, and a budget of the content scheme; in response to determining that the indicator corresponds to a command keyword recognized by the bid test component, determining a number of test content schemes to create and test budget values to assign to each test content scheme based upon the description of content, the user audience, and the budget; generating the number of test content schemes and assigning bid level values and portions of the budget as the test budget values to each test content scheme; calculating estimated budget utilization values for each of the test content schemes, wherein a first estimated budget utilization value corresponds to a first percentage of a first budget, assigned to a first test content scheme, that is to be utilized by a first point in time, and wherein a second estimated budget utilization value corresponds to a second percentage of a second budget, assigned to a second test content scheme, that is to be utilized by a second point in time; transmitting, over the network by the bid test component, the test content schemes to the content recommendation auction engine that implements bidding on opportunities to recommend content to users of the content provider service based upon the test content schemes; triggering, based upon the indicator, the content recommendation auction engine to accept instructions from the bid test component for implementing the test content schemes, wherein the computing device executes the bid testing component transmitting the instructions over the network to the content recommendation auction engine and remotely controlling how the content recommendation auction engine implements the test content schemes bidding on the opportunities to recommend the content to the users; determining test content scheme statistic data, received over the network by the bid test component from the content recommendation auction engine, derived from bidding data of the test content schemes being implemented and user action data for the content; analyzing the test content scheme statistic data for ranking the bid level values assigned to the test content schemes, wherein the first test content scheme is assigned a first rank based upon a first efficiency of the first test content scheme utilizing the first budget in relation to the first estimated budget utilization value, and wherein the second test content scheme is assigned a second rank based upon a second efficiency of the second test content scheme utilizing the second budget in relation to the second estimated budget utilization value; dynamically adjusting test budget values assigned to the test content schemes based upon the rankings; deactivating test content schemes having performance below a threshold based upon the rankings, wherein the first test content scheme is deactivated based upon the first rank being below a threshold and the second test content scheme is maintained based upon the second ranking not being below the threshold; and providing a recommendation, specifying optimal performance bidding parameters, bidding timeframes, and bid level values to implement for the content scheme, based upon ranked bid level values.
This invention relates to a system for optimizing content recommendation bidding in an auction-based advertising platform. The system addresses the challenge of efficiently testing and selecting optimal bidding strategies for content recommendations to maximize budget utilization and performance. A bid test component executes an API to monitor a storage repository of a content recommendation auction engine, detecting when a content scheme is tagged with an indicator triggering a notification. The notification includes details such as content description, target audience, and budget. Upon recognizing a command keyword in the indicator, the system determines the number of test content schemes to create and assigns test budget values based on the provided parameters. The system generates multiple test content schemes, each with different bid levels and budget allocations, and calculates estimated budget utilization for each. These test schemes are transmitted to the auction engine, which implements them for bidding on content recommendation opportunities. The bid test component remotely controls the auction engine's implementation of these schemes. Performance data, including bidding and user action metrics, is collected and analyzed to rank the test schemes based on efficiency in budget utilization. Underperforming schemes are deactivated, while high-performing ones are maintained. The system dynamically adjusts budget allocations based on rankings and provides recommendations for optimal bidding parameters, timeframes, and bid levels to maximize the original content scheme's performance. This approach automates the testing and optimization of bidding strategies, improving efficiency and effectiveness in content recommendation campaigns.
15. The non-transitory machine readable medium of claim 14 , wherein the operations comprise: adjusting a bid level value of a test content scheme based upon monitored content scheme statistic data for the test content scheme.
A system and method for optimizing digital content delivery involves dynamically adjusting bid levels for test content schemes based on performance metrics. The technology operates in the domain of online advertising or content distribution, where content providers aim to maximize engagement or conversion rates by testing different content variations. The problem addressed is the static allocation of resources to content schemes, which can lead to suboptimal performance and inefficient use of advertising budgets. The system monitors content scheme statistic data, such as click-through rates, conversion rates, or user engagement metrics, for a test content scheme. Based on this data, the system automatically adjusts the bid level value of the test content scheme to improve its performance. Higher-performing content schemes receive increased bid levels, while underperforming schemes may have their bids reduced or paused. This dynamic adjustment ensures that resources are allocated efficiently, maximizing the return on investment for content providers. The system may also include a content delivery network (CDN) or ad server that distributes the content schemes to users based on the adjusted bid levels. The monitoring and adjustment process is continuous, allowing the system to adapt to changing user behavior or market conditions in real time. This approach improves the overall effectiveness of content delivery campaigns by optimizing bid levels based on real-world performance data.
16. The non-transitory machine readable medium of claim 14 , wherein the operations comprise: adjusting a test budget value of a test content scheme based upon monitored content scheme statistic data for the test content scheme.
A system and method for optimizing test content schemes in software testing involves dynamically adjusting test budgets based on performance metrics. The technology addresses the challenge of efficiently allocating testing resources to maximize coverage and effectiveness while minimizing costs. The system monitors content scheme statistic data, which includes metrics such as test pass/fail rates, execution time, and resource utilization. Based on this data, the system adjusts the test budget value assigned to each test content scheme. A higher budget may be allocated to schemes that demonstrate higher effectiveness or criticality, while lower budgets may be assigned to less impactful or redundant tests. The adjustment process ensures that testing efforts are focused on the most valuable areas, improving overall test efficiency and reducing unnecessary resource consumption. The system may also incorporate historical data and predictive analytics to refine budget adjustments over time. This approach helps organizations balance thorough testing with cost constraints, particularly in large-scale or continuous testing environments.
17. The non-transitory machine readable medium of claim 14 , wherein the operations comprise: terminating a test content scheme based upon monitored content scheme statistic data for the test content scheme.
This invention relates to content delivery systems, specifically methods for managing and optimizing content schemes based on performance metrics. The technology addresses the problem of inefficient content distribution by dynamically adjusting or terminating content schemes that do not meet performance criteria. The system monitors content scheme statistic data, such as user engagement, load times, or error rates, to evaluate the effectiveness of a test content scheme. If the monitored data indicates poor performance, the system automatically terminates the underperforming scheme. This ensures that only high-performing content schemes remain active, improving overall system efficiency and user experience. The invention involves a non-transitory machine-readable medium storing instructions that, when executed, perform operations including monitoring content scheme statistics and terminating schemes that fail to meet predefined performance thresholds. The system may also compare the test content scheme against a baseline or control scheme to determine whether termination is necessary. By dynamically adjusting content distribution based on real-time performance data, the system optimizes resource allocation and enhances content delivery effectiveness.
18. The non-transitory machine readable medium of claim 14 , wherein the operations comprise: determining an estimated budget utilization based upon the test content scheme statistic data, wherein the estimated budget utilization specifies an amount, of a test budget value allocated to a test content scheme, that will be used by a point in time.
The invention relates to test budget management in software testing, specifically optimizing the allocation and utilization of testing resources. The problem addressed is inefficient test budget allocation, where resources may be underutilized or exhausted prematurely, leading to incomplete testing or wasted effort. The solution involves dynamically tracking and predicting test budget utilization based on statistical data from test content schemes. The system determines an estimated budget utilization by analyzing test content scheme statistics, which include metrics such as test case execution rates, resource consumption patterns, and historical usage data. This estimation predicts how much of the allocated test budget will be consumed by a specific point in time, allowing for proactive adjustments. The test content scheme defines the structure and scope of testing activities, including test cases, environments, and resource requirements. The system monitors these schemes in real-time or near-real-time to refine predictions and ensure optimal budget allocation. By continuously updating the estimated budget utilization, the system helps prevent budget overruns or shortages, improving testing efficiency and coverage. The invention is implemented via a non-transitory machine-readable medium, such as a software application or embedded system, that processes the statistical data to generate actionable insights for test managers. This approach ensures that testing resources are used effectively, aligning with project timelines and quality objectives.
19. The non-transitory machine readable medium of claim 18 , wherein the operations comprise: determining the estimated budget utilization based upon a velocity of budget utilization from a first point in time to a second point in time.
This invention relates to budget management systems, specifically for tracking and predicting budget utilization in project management or financial planning. The problem addressed is the need for accurate, real-time budget forecasting to prevent overspending or underutilization of resources. The system calculates an estimated budget utilization by analyzing the rate of budget consumption over a defined period, known as the velocity of budget utilization. This velocity is determined by comparing budget usage between two distinct time points, providing a dynamic assessment of spending trends. The system then uses this velocity to project future budget utilization, enabling proactive adjustments to spending plans. The invention improves upon traditional static budget tracking by incorporating time-based trends, allowing for more responsive financial decision-making. The solution is particularly useful in project management, where budgets must be closely monitored to ensure alignment with project milestones and deliverables. By continuously updating the velocity of budget utilization, the system provides a more accurate and adaptive forecast compared to fixed or historical budget assessments. The invention is implemented in a non-transitory machine-readable medium, ensuring scalability and integration with existing financial or project management software.
20. The non-transitory machine readable medium of claim 14 , wherein the operations comprise: determining a performance of a test content scheme based upon the test content scheme statistic data; responsive to the performance being below a first threshold, sending a first indicator to the content recommendation auction engine to display to the user; responsive to the performance exceeding a second threshold, sending a second indicator to the content recommendation auction engine to display to the user; and responsive to the performance being between the first threshold and the second threshold, sending a third indicator to the content recommendation auction engine to display to the user.
A system evaluates the performance of test content schemes using statistical data. The system monitors how well different content recommendation schemes perform, such as those used in online advertising or personalized content delivery. Performance is assessed by comparing statistical metrics against predefined thresholds. If performance falls below a first threshold, a first indicator is sent to a content recommendation auction engine, prompting it to display a notification to the user. If performance exceeds a second threshold, a second indicator is sent, triggering a different user notification. If performance lies between the two thresholds, a third indicator is sent, resulting in yet another type of user notification. This dynamic adjustment ensures that content recommendations are optimized based on real-time performance data, improving user engagement and system efficiency. The system may integrate with existing content delivery platforms to enhance recommendation accuracy and adaptability.
Unknown
December 15, 2020
Browse 5M+ US patents with plain-English claim translations and AI-generated analysis.